Overview

Dataset statistics

Number of variables14
Number of observations100000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 MiB
Average record size in memory120.0 B

Variable types

Numeric12
Categorical2

Alerts

SYM/H_INDEX_nT is highly overall correlated with 3-H_KP*10_ and 1 other fieldsHigh correlation
1-M_AE_nT is highly overall correlated with 3-H_KP*10_High correlation
400kmDensity is highly overall correlated with DAILY_SUNSPOT_NO_ and 4 other fieldsHigh correlation
DAILY_SUNSPOT_NO_ is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
DAILY_F10.7_ is highly overall correlated with DAILY_SUNSPOT_NO_ and 3 other fieldsHigh correlation
3-H_KP*10_ is highly overall correlated with SYM/H_INDEX_nT and 2 other fieldsHigh correlation
3-H_AP_nT is highly overall correlated with SYM/H_INDEX_nT and 1 other fieldsHigh correlation
SOLAR_LYMAN-ALPHA_W/m^2 is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
mg_index (core to wing ratio (unitless)) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
irradiance (W/m^2/nm) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
storm is highly overall correlated with storm phaseHigh correlation
storm phase is highly overall correlated with stormHigh correlation
d_diff is highly overall correlated with 400kmDensityHigh correlation
SYM/H_INDEX_nT has 2687 (2.7%) zerosZeros
DAILY_SUNSPOT_NO_ has 24213 (24.2%) zerosZeros
3-H_KP*10_ has 8307 (8.3%) zerosZeros
3-H_AP_nT has 8307 (8.3%) zerosZeros
d_diff has 1560 (1.6%) zerosZeros

Reproduction

Analysis started2023-02-24 17:03:50.780885
Analysis finished2023-02-24 17:04:15.980555
Duration25.2 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

SYM/H_INDEX_nT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct309
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.45304
Minimum-479
Maximum143
Zeros2687
Zeros (%)2.7%
Negative76299
Negative (%)76.3%
Memory size1.5 MiB
2023-02-24T12:04:16.071032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-479
5-th percentile-42
Q1-18
median-8
Q3-1
95-th percentile9
Maximum143
Range622
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.302996
Coefficient of variation (CV)-1.6854037
Kurtosis57.10223
Mean-11.45304
Median Absolute Deviation (MAD)8
Skewness-4.4587953
Sum-1145304
Variance372.60566
MonotonicityNot monotonic
2023-02-24T12:04:16.207543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3 3560
 
3.6%
-4 3502
 
3.5%
-7 3497
 
3.5%
-5 3480
 
3.5%
-2 3393
 
3.4%
-6 3392
 
3.4%
-8 3324
 
3.3%
-1 3160
 
3.2%
-9 3103
 
3.1%
-10 3085
 
3.1%
Other values (299) 66504
66.5%
ValueCountFrequency (%)
-479 1
< 0.1%
-452 1
< 0.1%
-446 1
< 0.1%
-445 1
< 0.1%
-440 1
< 0.1%
-429 1
< 0.1%
-412 1
< 0.1%
-410 1
< 0.1%
-389 1
< 0.1%
-386 2
< 0.1%
ValueCountFrequency (%)
143 1
 
< 0.1%
78 1
 
< 0.1%
73 1
 
< 0.1%
71 3
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
63 2
< 0.1%
62 1
 
< 0.1%
60 1
 
< 0.1%
59 2
< 0.1%

1-M_AE_nT
Real number (ℝ)

Distinct1438
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.9791
Minimum1
Maximum3561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-24T12:04:16.339759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q138
median87
Q3233
95-th percentile617
Maximum3561
Range3560
Interquartile range (IQR)195

Descriptive statistics

Standard deviation213.14998
Coefficient of variation (CV)1.2181453
Kurtosis9.7070586
Mean174.9791
Median Absolute Deviation (MAD)61
Skewness2.5007307
Sum17497910
Variance45432.912
MonotonicityNot monotonic
2023-02-24T12:04:16.462023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 928
 
0.9%
38 912
 
0.9%
29 910
 
0.9%
30 907
 
0.9%
34 907
 
0.9%
25 898
 
0.9%
33 891
 
0.9%
31 888
 
0.9%
37 885
 
0.9%
27 872
 
0.9%
Other values (1428) 91002
91.0%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 10
 
< 0.1%
3 59
 
0.1%
4 111
 
0.1%
5 180
 
0.2%
6 271
0.3%
7 364
0.4%
8 403
0.4%
9 463
0.5%
10 539
0.5%
ValueCountFrequency (%)
3561 1
< 0.1%
3338 1
< 0.1%
3287 1
< 0.1%
2673 1
< 0.1%
2658 1
< 0.1%
2487 1
< 0.1%
2262 1
< 0.1%
2251 1
< 0.1%
2242 1
< 0.1%
2146 1
< 0.1%

400kmDensity
Real number (ℝ)

Distinct99401
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4819581 × 10-12
Minimum1.004137 × 10-15
Maximum2.217417 × 10-11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-24T12:04:16.610893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.004137 × 10-15
5-th percentile2.0372052 × 10-13
Q14.9608763 × 10-13
median9.6091535 × 10-13
Q31.932812 × 10-12
95-th percentile4.5967633 × 10-12
Maximum2.217417 × 10-11
Range2.2173166 × 10-11
Interquartile range (IQR)1.4367244 × 10-12

Descriptive statistics

Standard deviation1.4682536 × 10-12
Coefficient of variation (CV)0.99075244
Kurtosis0
Mean1.4819581 × 10-12
Median Absolute Deviation (MAD)5.7465875 × 10-13
Skewness0
Sum1.4819581 × 10-7
Variance2.1557687 × 10-24
MonotonicityNot monotonic
2023-02-24T12:04:16.742942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.005954 × 10-123
 
< 0.1%
1.316555 × 10-123
 
< 0.1%
1.256108 × 10-123
 
< 0.1%
6.563082 × 10-132
 
< 0.1%
8.230663 × 10-132
 
< 0.1%
3.235107 × 10-122
 
< 0.1%
4.881847 × 10-132
 
< 0.1%
1.236913 × 10-122
 
< 0.1%
1.919651 × 10-122
 
< 0.1%
1.768371 × 10-122
 
< 0.1%
Other values (99391) 99977
> 99.9%
ValueCountFrequency (%)
1.004137 × 10-151
< 0.1%
1.987595 × 10-151
< 0.1%
2.736538 × 10-152
< 0.1%
3.11584 × 10-151
< 0.1%
3.844457 × 10-151
< 0.1%
3.905613 × 10-151
< 0.1%
4.735086 × 10-151
< 0.1%
5.161798 × 10-151
< 0.1%
5.407496 × 10-151
< 0.1%
7.153326 × 10-151
< 0.1%
ValueCountFrequency (%)
2.217417 × 10-111
< 0.1%
2.180779 × 10-111
< 0.1%
2.054336 × 10-111
< 0.1%
1.763653 × 10-111
< 0.1%
1.690277 × 10-111
< 0.1%
1.666842 × 10-111
< 0.1%
1.56233 × 10-111
< 0.1%
1.467527 × 10-111
< 0.1%
1.441503 × 10-111
< 0.1%
1.42635 × 10-111
< 0.1%

AveDragCoef
Real number (ℝ)

Distinct1029
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1142871
Minimum2.283
Maximum3.919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-24T12:04:17.216111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.283
5-th percentile2.886
Q12.966
median3.08
Q33.24
95-th percentile3.443
Maximum3.919
Range1.636
Interquartile range (IQR)0.274

Descriptive statistics

Standard deviation0.17940781
Coefficient of variation (CV)0.057607987
Kurtosis-0.071431206
Mean3.1142871
Median Absolute Deviation (MAD)0.13
Skewness0.68833354
Sum311428.71
Variance0.032187163
MonotonicityNot monotonic
2023-02-24T12:04:17.350970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.931 297
 
0.3%
2.928 294
 
0.3%
2.95 293
 
0.3%
2.963 291
 
0.3%
2.935 288
 
0.3%
2.948 285
 
0.3%
2.937 285
 
0.3%
2.924 282
 
0.3%
2.972 282
 
0.3%
2.938 281
 
0.3%
Other values (1019) 97122
97.1%
ValueCountFrequency (%)
2.283 1
< 0.1%
2.299 1
< 0.1%
2.32 1
< 0.1%
2.347 1
< 0.1%
2.362 1
< 0.1%
2.374 1
< 0.1%
2.428 1
< 0.1%
2.436 1
< 0.1%
2.44 1
< 0.1%
2.451 1
< 0.1%
ValueCountFrequency (%)
3.919 1
< 0.1%
3.9 1
< 0.1%
3.884 1
< 0.1%
3.88 1
< 0.1%
3.866 1
< 0.1%
3.851 1
< 0.1%
3.849 1
< 0.1%
3.846 1
< 0.1%
3.83 2
< 0.1%
3.826 1
< 0.1%

DAILY_SUNSPOT_NO_
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct214
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.4731
Minimum0
Maximum281
Zeros24213
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-24T12:04:17.485907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median31
Q372
95-th percentile149
Maximum281
Range281
Interquartile range (IQR)64

Descriptive statistics

Standard deviation50.454803
Coefficient of variation (CV)1.0628083
Kurtosis1.4655233
Mean47.4731
Median Absolute Deviation (MAD)31
Skewness1.3051109
Sum4747310
Variance2545.6872
MonotonicityNot monotonic
2023-02-24T12:04:17.600715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24213
 
24.2%
13 2441
 
2.4%
12 2389
 
2.4%
15 1995
 
2.0%
14 1873
 
1.9%
18 1479
 
1.5%
16 1342
 
1.3%
26 1328
 
1.3%
11 1196
 
1.2%
23 978
 
1.0%
Other values (204) 60766
60.8%
ValueCountFrequency (%)
0 24213
24.2%
5 52
 
0.1%
6 151
 
0.2%
7 366
 
0.4%
8 287
 
0.3%
9 478
 
0.5%
10 810
 
0.8%
11 1196
 
1.2%
12 2389
 
2.4%
13 2441
 
2.4%
ValueCountFrequency (%)
281 28
 
< 0.1%
279 38
< 0.1%
270 32
< 0.1%
267 37
< 0.1%
263 19
 
< 0.1%
252 34
< 0.1%
250 71
0.1%
248 56
0.1%
247 61
0.1%
239 20
 
< 0.1%

DAILY_F10.7_
Real number (ℝ)

Distinct927
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.339171
Minimum65.1
Maximum999.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-24T12:04:17.727886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum65.1
5-th percentile67.5
Q171.5
median85.1
Q3111
95-th percentile157.8
Maximum999.9
Range934.8
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation52.35277
Coefficient of variation (CV)0.53783866
Kurtosis192.9102
Mean97.339171
Median Absolute Deviation (MAD)15.5
Skewness11.606651
Sum9733917.1
Variance2740.8125
MonotonicityNot monotonic
2023-02-24T12:04:17.845772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.3 795
 
0.8%
68 785
 
0.8%
69.8 757
 
0.8%
68.8 723
 
0.7%
69.5 699
 
0.7%
68.2 622
 
0.6%
70.5 619
 
0.6%
68.5 618
 
0.6%
67.4 614
 
0.6%
70.3 611
 
0.6%
Other values (917) 93157
93.2%
ValueCountFrequency (%)
65.1 32
 
< 0.1%
65.2 32
 
< 0.1%
65.5 33
 
< 0.1%
65.6 31
 
< 0.1%
65.8 71
 
0.1%
65.9 52
 
0.1%
66 101
 
0.1%
66.1 97
 
0.1%
66.2 269
0.3%
66.3 237
0.2%
ValueCountFrequency (%)
999.9 221
0.2%
275.4 32
 
< 0.1%
270.9 34
 
< 0.1%
267.6 39
 
< 0.1%
254 30
 
< 0.1%
246.9 32
 
< 0.1%
245.2 20
 
< 0.1%
242.6 33
 
< 0.1%
240.6 29
 
< 0.1%
232.8 28
 
< 0.1%

3-H_KP*10_
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.09961
Minimum0
Maximum90
Zeros8307
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-24T12:04:17.956967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median17
Q327
95-th percentile43
Maximum90
Range90
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.205365
Coefficient of variation (CV)0.78484369
Kurtosis0.78776292
Mean18.09961
Median Absolute Deviation (MAD)10
Skewness0.91919815
Sum1809961
Variance201.79239
MonotonicityNot monotonic
2023-02-24T12:04:18.054440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
7 11271
11.3%
3 11238
11.2%
10 9704
9.7%
13 8645
8.6%
0 8307
8.3%
17 7957
8.0%
20 7401
7.4%
23 6504
 
6.5%
27 6051
 
6.1%
30 5266
 
5.3%
Other values (18) 17656
17.7%
ValueCountFrequency (%)
0 8307
8.3%
3 11238
11.2%
7 11271
11.3%
10 9704
9.7%
13 8645
8.6%
17 7957
8.0%
20 7401
7.4%
23 6504
6.5%
27 6051
6.1%
30 5266
5.3%
ValueCountFrequency (%)
90 13
 
< 0.1%
87 39
 
< 0.1%
83 42
 
< 0.1%
80 29
 
< 0.1%
77 68
 
0.1%
73 114
 
0.1%
70 122
0.1%
67 109
 
0.1%
63 177
0.2%
60 303
0.3%

3-H_AP_nT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.5451
Minimum0
Maximum400
Zeros8307
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-24T12:04:18.158120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q312
95-th percentile32
Maximum400
Range400
Interquartile range (IQR)9

Descriptive statistics

Standard deviation17.207819
Coefficient of variation (CV)1.6318308
Kurtosis98.748517
Mean10.5451
Median Absolute Deviation (MAD)3
Skewness7.5595747
Sum1054510
Variance296.10905
MonotonicityNot monotonic
2023-02-24T12:04:18.272810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3 11271
11.3%
2 11238
11.2%
4 9704
9.7%
5 8645
8.6%
0 8307
8.3%
6 7957
8.0%
7 7401
7.4%
9 6504
 
6.5%
12 6051
 
6.1%
15 5266
 
5.3%
Other values (18) 17656
17.7%
ValueCountFrequency (%)
0 8307
8.3%
2 11238
11.2%
3 11271
11.3%
4 9704
9.7%
5 8645
8.6%
6 7957
8.0%
7 7401
7.4%
9 6504
6.5%
12 6051
6.1%
15 5266
5.3%
ValueCountFrequency (%)
400 13
 
< 0.1%
300 39
 
< 0.1%
236 42
 
< 0.1%
207 29
 
< 0.1%
179 68
 
0.1%
154 114
 
0.1%
132 122
0.1%
111 109
 
0.1%
94 177
0.2%
80 303
0.3%

SOLAR_LYMAN-ALPHA_W/m^2
Real number (ℝ)

Distinct1687
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0068232629
Minimum0.00588
Maximum0.009751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-24T12:04:18.422911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00588
5-th percentile0.005984
Q10.006174
median0.006575
Q30.007317
95-th percentile0.008347
Maximum0.009751
Range0.003871
Interquartile range (IQR)0.001143

Descriptive statistics

Standard deviation0.00077766229
Coefficient of variation (CV)0.1139722
Kurtosis0.40723712
Mean0.0068232629
Median Absolute Deviation (MAD)0.00051
Skewness0.99148058
Sum682.32629
Variance6.0475864 × 10-7
MonotonicityNot monotonic
2023-02-24T12:04:18.576287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00602 329
 
0.3%
0.006027 318
 
0.3%
0.005978 299
 
0.3%
0.005991 297
 
0.3%
0.006047 291
 
0.3%
0.006008 269
 
0.3%
0.006013 252
 
0.3%
0.006005 240
 
0.2%
0.006 240
 
0.2%
0.006006 237
 
0.2%
Other values (1677) 97228
97.2%
ValueCountFrequency (%)
0.00588 29
 
< 0.1%
0.005897 29
 
< 0.1%
0.005898 29
 
< 0.1%
0.005904 29
 
< 0.1%
0.005907 64
0.1%
0.005908 20
 
< 0.1%
0.005909 25
 
< 0.1%
0.00591 109
0.1%
0.005912 24
 
< 0.1%
0.005913 32
 
< 0.1%
ValueCountFrequency (%)
0.009751 28
< 0.1%
0.00974 33
< 0.1%
0.00972 34
< 0.1%
0.009662 32
< 0.1%
0.009581 32
< 0.1%
0.009577 28
< 0.1%
0.009555 35
< 0.1%
0.00954 27
< 0.1%
0.009511 36
< 0.1%
0.009483 19
< 0.1%
Distinct2311
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26856276
Minimum0.26295999
Maximum0.28494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-24T12:04:18.748103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.26295999
5-th percentile0.26374211
Q10.2647
median0.26703185
Q30.27149001
95-th percentile0.27772636
Maximum0.28494
Range0.02198001
Interquartile range (IQR)0.00679001

Descriptive statistics

Standard deviation0.0045745963
Coefficient of variation (CV)0.017033621
Kurtosis0.15249675
Mean0.26856276
Median Absolute Deviation (MAD)0.0027028
Skewness0.97548528
Sum26856.276
Variance2.0926932 × 10-5
MonotonicityNot monotonic
2023-02-24T12:04:18.873859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26708001 298
 
0.3%
0.26403001 259
 
0.3%
0.26469001 232
 
0.2%
0.26418999 232
 
0.2%
0.26475999 209
 
0.2%
0.26559001 206
 
0.2%
0.26697999 203
 
0.2%
0.26532999 200
 
0.2%
0.26418 198
 
0.2%
0.26576 195
 
0.2%
Other values (2301) 97768
97.8%
ValueCountFrequency (%)
0.26295999 32
< 0.1%
0.26299 35
< 0.1%
0.26300001 34
< 0.1%
0.26304999 27
< 0.1%
0.26306999 31
< 0.1%
0.26308 25
< 0.1%
0.26309001 24
 
< 0.1%
0.26311001 36
< 0.1%
0.26312 32
< 0.1%
0.26313001 62
0.1%
ValueCountFrequency (%)
0.28494 33
< 0.1%
0.28485999 28
< 0.1%
0.28428999 32
< 0.1%
0.28426999 34
< 0.1%
0.2841 28
< 0.1%
0.28386 35
< 0.1%
0.28376999 27
< 0.1%
0.28373272 27
< 0.1%
0.28373 29
< 0.1%
0.28360999 29
< 0.1%

irradiance (W/m^2/nm)
Real number (ℝ)

Distinct3231
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0055138164
Minimum0.0048730583
Maximum0.0073493496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-24T12:04:18.998722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0048730583
5-th percentile0.0049233721
Q10.0050501884
median0.0053247637
Q30.005854608
95-th percentile0.006616591
Maximum0.0073493496
Range0.0024762913
Interquartile range (IQR)0.00080441963

Descriptive statistics

Standard deviation0.00054600764
Coefficient of variation (CV)0.099025357
Kurtosis0.13560043
Mean0.0055138164
Median Absolute Deviation (MAD)0.00035667792
Skewness0.93446856
Sum551.38164
Variance2.9812434 × 10-7
MonotonicityNot monotonic
2023-02-24T12:04:19.127233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00495163817 106
 
0.1%
0.00491212029 92
 
0.1%
0.004914534744 82
 
0.1%
0.004930291791 82
 
0.1%
0.005239969585 79
 
0.1%
0.004920041654 77
 
0.1%
0.004962003324 75
 
0.1%
0.004922427703 72
 
0.1%
0.004931729753 72
 
0.1%
0.004975029733 70
 
0.1%
Other values (3221) 99193
99.2%
ValueCountFrequency (%)
0.004873058293 30
< 0.1%
0.004877128173 36
< 0.1%
0.004877185915 37
< 0.1%
0.004877588246 22
< 0.1%
0.004881324712 15
< 0.1%
0.004881698173 13
 
< 0.1%
0.004881755915 24
< 0.1%
0.00488556223 34
< 0.1%
0.004885710776 29
< 0.1%
0.004885739647 31
< 0.1%
ValueCountFrequency (%)
0.007349349558 34
< 0.1%
0.00734248152 30
< 0.1%
0.007334709167 35
< 0.1%
0.007301890757 34
< 0.1%
0.007268224377 36
< 0.1%
0.007266042288 26
< 0.1%
0.007259562146 37
< 0.1%
0.007257604506 22
< 0.1%
0.007247306872 27
< 0.1%
0.007218547165 28
< 0.1%

storm
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
56084 
-1
43916 

Length

Max length2
Median length1
Mean length1.43916
Min length1

Characters and Unicode

Total characters143916
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row-1

Common Values

ValueCountFrequency (%)
1 56084
56.1%
-1 43916
43.9%

Length

2023-02-24T12:04:19.241323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T12:04:19.336927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 100000
69.5%
- 43916
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100000
69.5%
Dash Punctuation 43916
30.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 100000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43916
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 143916
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 100000
69.5%
- 43916
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 100000
69.5%
- 43916
30.5%

storm phase
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
-1
43916 
2
33891 
1
22193 

Length

Max length2
Median length1
Mean length1.43916
Min length1

Characters and Unicode

Total characters143916
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row-1

Common Values

ValueCountFrequency (%)
-1 43916
43.9%
2 33891
33.9%
1 22193
22.2%

Length

2023-02-24T12:04:19.416806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T12:04:19.521926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 66109
66.1%
2 33891
33.9%

Most occurring characters

ValueCountFrequency (%)
1 66109
45.9%
- 43916
30.5%
2 33891
23.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100000
69.5%
Dash Punctuation 43916
30.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 66109
66.1%
2 33891
33.9%
Dash Punctuation
ValueCountFrequency (%)
- 43916
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 143916
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 66109
45.9%
- 43916
30.5%
2 33891
23.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 66109
45.9%
- 43916
30.5%
2 33891
23.5%

d_diff
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96525
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4561046 × 10-17
Minimum-5.861174 × 10-12
Maximum1.0747433 × 10-11
Zeros1560
Zeros (%)1.6%
Negative47851
Negative (%)47.9%
Memory size1.5 MiB
2023-02-24T12:04:19.624251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-5.861174 × 10-12
5-th percentile-1.6738071 × 10-13
Q1-3.610285 × 10-14
median6.9424 × 10-16
Q33.7980475 × 10-14
95-th percentile1.6017985 × 10-13
Maximum1.0747433 × 10-11
Range1.6608607 × 10-11
Interquartile range (IQR)7.4083325 × 10-14

Descriptive statistics

Standard deviation1.7051524 × 10-13
Coefficient of variation (CV)6942.5071
Kurtosis0
Mean2.4561046 × 10-17
Median Absolute Deviation (MAD)3.7057 × 10-14
Skewness0
Sum2.4561046 × 10-12
Variance2.9075447 × 10-26
MonotonicityNot monotonic
2023-02-24T12:04:19.747765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1560
 
1.6%
-4.3962 × 10-144
 
< 0.1%
2.4991 × 10-144
 
< 0.1%
2.2555 × 10-144
 
< 0.1%
4.1174 × 10-143
 
< 0.1%
-2.2018 × 10-143
 
< 0.1%
-2.377 × 10-143
 
< 0.1%
-2.7628 × 10-143
 
< 0.1%
7.208 × 10-153
 
< 0.1%
-2.6983 × 10-143
 
< 0.1%
Other values (96515) 98410
98.4%
ValueCountFrequency (%)
-5.861174 × 10-121
< 0.1%
-5.2984891 × 10-121
< 0.1%
-3.7811089 × 10-121
< 0.1%
-3.735095 × 10-121
< 0.1%
-3.4274175 × 10-121
< 0.1%
-3.2566145 × 10-121
< 0.1%
-3.14226 × 10-121
< 0.1%
-3.1299126 × 10-121
< 0.1%
-2.964886 × 10-121
< 0.1%
-2.96378 × 10-121
< 0.1%
ValueCountFrequency (%)
1.0747433 × 10-111
< 0.1%
6.9862178 × 10-121
< 0.1%
6.35863 × 10-121
< 0.1%
5.596408 × 10-121
< 0.1%
4.953065 × 10-121
< 0.1%
4.903684 × 10-121
< 0.1%
4.261493 × 10-121
< 0.1%
4.105053 × 10-121
< 0.1%
3.5145884 × 10-121
< 0.1%
3.2611512 × 10-121
< 0.1%

Interactions

2023-02-24T12:04:14.109200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:03:58.752232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:00.215824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:01.585489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:03.204858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:04.596994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:05.911448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:07.203938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:08.461680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:10.053974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:11.447993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:12.790825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:14.219815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:03:58.878961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:00.331844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:01.694401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:03.319856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:04.705741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:06.019379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:07.310810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:08.579015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:10.169833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:11.559828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:12.901060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:14.332753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:03:58.999913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:00.451003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:01.804035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:03.438207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:04.820910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:06.133287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:07.419096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:08.698862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:10.288968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:11.677236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:13.013964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:14.436904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:03:59.118859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:00.559328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:01.906683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:03.548970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:04.929046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:06.235934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:07.517848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:08.807709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:10.398659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:11.781496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:13.116684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:14.555457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:03:59.246344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:00.683206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:02.021499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:03.669070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:05.045143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:06.361371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:07.632538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:08.931153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:10.522920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:11.921557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:13.232773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:14.664337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:03:59.358266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:00.798046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:02.124957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:03.780965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:05.150858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:06.465837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:07.734854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:09.253727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:10.646352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:12.031726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:13.336936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:14.769480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:03:59.472214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:00.906450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:02.237309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:03.895055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:05.251372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:06.564859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:07.829945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:09.359284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:10.751362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:12.135474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:13.438138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:14.871719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:03:59.589494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:01.007850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:02.335861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:04.000988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:05.351483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:06.661408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:07.927128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:09.464844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:10.859806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:12.235189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:13.539429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:14.987411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:03:59.716795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:01.128853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:02.721036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:04.125833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:05.466851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:06.772083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:08.037487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:09.584407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:10.984084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:12.349365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:13.662318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:15.116291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:03:59.858906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:01.246551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:02.846176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:04.253158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:05.586561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:06.887598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:08.153394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:09.713863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:11.106786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:12.469050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:13.783852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:15.222417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:03:59.986420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:01.355515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:02.960991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:04.364273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:05.693066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:06.996085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:08.254025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:09.827136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:11.218362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:12.573451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:13.888418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:15.328037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:00.101175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:01.466890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:03.074718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:04.473520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:05.797967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:07.096872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:08.354834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:09.934526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:11.328835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:12.679982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T12:04:13.993482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-24T12:04:19.884084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
SYM/H_INDEX_nT1-M_AE_nT400kmDensityAveDragCoefDAILY_SUNSPOT_NO_DAILY_F10.7_3-H_KP*10_3-H_AP_nTSOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)d_diffstormstorm phase
SYM/H_INDEX_nT1.000-0.526-0.302-0.068-0.171-0.185-0.510-0.510-0.201-0.165-0.202-0.0020.1660.144
1-M_AE_nT-0.5261.0000.3930.1120.3060.3250.7840.7840.3410.2840.3490.0040.2270.164
400kmDensity-0.3020.3931.0000.1880.7710.8210.4210.4210.8480.8150.8560.0480.1690.120
AveDragCoef-0.0680.1120.1881.0000.1830.1930.1170.1170.2000.1650.206-0.0170.1090.079
DAILY_SUNSPOT_NO_-0.1710.3060.7710.1831.0000.9360.2950.2950.9060.8950.8940.0080.2120.156
DAILY_F10.7_-0.1850.3250.8210.1930.9361.0000.3140.3140.9550.9380.9490.0100.0650.058
3-H_KP*10_-0.5100.7840.4210.1170.2950.3141.0001.0000.3310.2690.3410.0040.3570.264
3-H_AP_nT-0.5100.7840.4210.1170.2950.3141.0001.0000.3310.2690.3410.0040.1350.102
SOLAR_LYMAN-ALPHA_W/m^2-0.2010.3410.8480.2000.9060.9550.3310.3311.0000.9510.9920.0080.2320.169
mg_index (core to wing ratio (unitless))-0.1650.2840.8150.1650.8950.9380.2690.2690.9511.0000.9430.0070.1970.142
irradiance (W/m^2/nm)-0.2020.3490.8560.2060.8940.9490.3410.3410.9920.9431.0000.0080.2460.179
d_diff-0.0020.0040.048-0.0170.0080.0100.0040.0040.0080.0070.0081.0000.0510.037
storm0.1660.2270.1690.1090.2120.0650.3570.1350.2320.1970.2460.0511.0001.000
storm phase0.1440.1640.1200.0790.1560.0580.2640.1020.1690.1420.1790.0371.0001.000
2023-02-24T12:04:20.134641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
SYM/H_INDEX_nT1-M_AE_nT400kmDensityAveDragCoefDAILY_SUNSPOT_NO_DAILY_F10.7_3-H_KP*10_3-H_AP_nTSOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
SYM/H_INDEX_nT1.000-0.548-0.348-0.071-0.188-0.128-0.568-0.657-0.216-0.184-0.213-0.281-0.3340.004
1-M_AE_nT-0.5481.0000.3050.0820.2280.1370.7490.6690.2570.2110.2650.2810.277-0.005
400kmDensity-0.3480.3051.0000.1800.7370.4560.3790.3370.8180.7920.8270.2010.1980.061
AveDragCoef-0.0710.0820.1801.0000.1790.1050.0960.0820.1910.1530.1990.0580.055-0.006
DAILY_SUNSPOT_NO_-0.1880.2280.7370.1791.0000.5340.2740.2130.9040.8980.8840.1850.175-0.003
DAILY_F10.7_-0.1280.1370.4560.1050.5341.0000.1750.1430.5510.5430.5410.1370.134-0.001
3-H_KP*10_-0.5680.7490.3790.0960.2740.1751.0000.7930.3180.2540.3310.3570.357-0.002
3-H_AP_nT-0.6570.6690.3370.0820.2130.1430.7931.0000.2290.1870.2340.2470.2350.002
SOLAR_LYMAN-ALPHA_W/m^2-0.2160.2570.8180.1910.9040.5510.3180.2291.0000.9630.9880.2120.203-0.004
mg_index (core to wing ratio (unitless))-0.1840.2110.7920.1530.8980.5430.2540.1870.9631.0000.9490.1540.147-0.003
irradiance (W/m^2/nm)-0.2130.2650.8270.1990.8840.5410.3310.2340.9880.9491.0000.2210.210-0.002
storm-0.2810.2810.2010.0580.1850.1370.3570.2470.2120.1540.2211.0000.9620.001
storm phase-0.3340.2770.1980.0550.1750.1340.3570.2350.2030.1470.2100.9621.0000.001
d_diff0.004-0.0050.061-0.006-0.003-0.001-0.0020.002-0.004-0.003-0.0020.0010.0011.000
2023-02-24T12:04:20.354419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
SYM/H_INDEX_nT1-M_AE_nT400kmDensityAveDragCoefDAILY_SUNSPOT_NO_DAILY_F10.7_3-H_KP*10_3-H_AP_nTSOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
SYM/H_INDEX_nT1.000-0.526-0.302-0.068-0.171-0.185-0.510-0.510-0.201-0.165-0.202-0.329-0.426-0.002
1-M_AE_nT-0.5261.0000.3930.1120.3060.3250.7840.7840.3410.2840.3490.3180.3220.004
400kmDensity-0.3020.3931.0000.1880.7710.8210.4210.4210.8480.8150.8560.2650.2550.048
AveDragCoef-0.0680.1120.1881.0000.1830.1930.1170.1170.2000.1650.2060.0780.072-0.017
DAILY_SUNSPOT_NO_-0.1710.3060.7710.1831.0000.9360.2950.2950.9060.8950.8940.2090.1860.008
DAILY_F10.7_-0.1850.3250.8210.1930.9361.0000.3140.3140.9550.9380.9490.2270.2010.010
3-H_KP*10_-0.5100.7840.4210.1170.2950.3141.0001.0000.3310.2690.3410.3590.3600.004
3-H_AP_nT-0.5100.7840.4210.1170.2950.3141.0001.0000.3310.2690.3410.3590.3600.004
SOLAR_LYMAN-ALPHA_W/m^2-0.2010.3410.8480.2000.9060.9550.3310.3311.0000.9510.9920.2370.2150.008
mg_index (core to wing ratio (unitless))-0.1650.2840.8150.1650.8950.9380.2690.2690.9511.0000.9430.1750.1560.007
irradiance (W/m^2/nm)-0.2020.3490.8560.2060.8940.9490.3410.3410.9920.9431.0000.2430.2190.008
storm-0.3290.3180.2650.0780.2090.2270.3590.3590.2370.1750.2431.0000.9240.004
storm phase-0.4260.3220.2550.0720.1860.2010.3600.3600.2150.1560.2190.9241.0000.006
d_diff-0.0020.0040.048-0.0170.0080.0100.0040.0040.0080.0070.0080.0040.0061.000
2023-02-24T12:04:20.554556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
SYM/H_INDEX_nT1-M_AE_nT400kmDensityAveDragCoefDAILY_SUNSPOT_NO_DAILY_F10.7_3-H_KP*10_3-H_AP_nTSOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
SYM/H_INDEX_nT1.000-0.370-0.209-0.046-0.119-0.126-0.375-0.375-0.136-0.111-0.137-0.271-0.324-0.001
1-M_AE_nT-0.3701.0000.2670.0750.2110.2210.6080.6080.2310.1900.2360.2600.2480.003
400kmDensity-0.2090.2671.0000.1290.5760.6160.2980.2980.6480.6080.6580.2160.1970.034
AveDragCoef-0.0460.0750.1291.0000.1280.1320.0810.0810.1370.1120.1410.0640.056-0.012
DAILY_SUNSPOT_NO_-0.1190.2110.5760.1281.0000.7910.2100.2100.7390.7220.7210.1760.1480.006
DAILY_F10.7_-0.1260.2210.6160.1320.7911.0000.2190.2190.8100.7790.7990.1850.1560.007
3-H_KP*10_-0.3750.6080.2980.0810.2100.2191.0001.0000.2300.1850.2370.3040.2850.003
3-H_AP_nT-0.3750.6080.2980.0810.2100.2191.0001.0000.2300.1850.2370.3040.2850.003
SOLAR_LYMAN-ALPHA_W/m^2-0.1360.2310.6480.1370.7390.8100.2300.2301.0000.8050.9260.1940.1670.006
mg_index (core to wing ratio (unitless))-0.1110.1900.6080.1120.7220.7790.1850.1850.8051.0000.7840.1430.1210.005
irradiance (W/m^2/nm)-0.1370.2360.6580.1410.7210.7990.2370.2370.9260.7841.0000.1980.1700.006
storm-0.2710.2600.2160.0640.1760.1850.3040.3040.1940.1430.1981.0000.8750.003
storm phase-0.3240.2480.1970.0560.1480.1560.2850.2850.1670.1210.1700.8751.0000.004
d_diff-0.0010.0030.034-0.0120.0060.0070.0030.0030.0060.0050.0060.0030.0041.000
2023-02-24T12:04:20.763919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
SYM/H_INDEX_nT1-M_AE_nT400kmDensityAveDragCoefDAILY_SUNSPOT_NO_DAILY_F10.7_3-H_KP*10_3-H_AP_nTSOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
SYM/H_INDEX_nT1.0000.3670.4750.0840.2910.2830.7450.6320.2110.2040.2100.2170.2350.151
1-M_AE_nT0.3671.0000.3170.0730.1830.1670.5550.4680.1730.1620.1740.2270.3490.148
400kmDensity0.4750.3171.0000.1690.5070.4760.3500.3500.5760.5570.5830.1700.2650.510
AveDragCoef0.0840.0730.1691.0000.2500.1510.1720.0500.2930.2480.3160.1420.1320.080
DAILY_SUNSPOT_NO_0.2910.1830.5070.2501.0000.7010.3590.2170.8820.8570.8500.2770.2540.086
DAILY_F10.7_0.2830.1670.4760.1510.7011.0000.2450.4470.6600.6260.6160.0990.0620.079
3-H_KP*10_0.7450.5550.3500.1720.3590.2451.0000.8490.3570.3230.3590.4640.4010.167
3-H_AP_nT0.6320.4680.3500.0500.2170.4470.8491.0000.1390.1310.1320.1800.1590.184
SOLAR_LYMAN-ALPHA_W/m^20.2110.1730.5760.2930.8820.6600.3570.1391.0000.9530.9720.3030.2720.108
mg_index (core to wing ratio (unitless))0.2040.1620.5570.2480.8570.6260.3230.1310.9531.0000.9240.2560.2320.093
irradiance (W/m^2/nm)0.2100.1740.5830.3160.8500.6160.3590.1320.9720.9241.0000.3210.2870.104
storm0.2170.2270.1700.1420.2770.0990.4640.1800.3030.2560.3211.0001.0000.052
storm phase0.2350.3490.2650.1320.2540.0620.4010.1590.2720.2320.2871.0001.0000.086
d_diff0.1510.1480.5100.0800.0860.0790.1670.1840.1080.0930.1040.0520.0861.000
2023-02-24T12:04:20.934867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
stormstorm phase
storm1.0001.000
storm phase1.0001.000

Missing values

2023-02-24T12:04:15.484122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-24T12:04:15.763315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SYM/H_INDEX_nT1-M_AE_nT400kmDensityAveDragCoefDAILY_SUNSPOT_NO_DAILY_F10.7_3-H_KP*10_3-H_AP_nTSOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
3086453-2.0200.01.673935e-123.397124.0140.920.07.00.0077310.2721800.006174113.383500e-14
4464047-12.098.03.394151e-122.93856.0105.210.04.00.0073360.2735810.00581811-7.174700e-14
3397990-9.043.05.365465e-133.01528.089.47.03.00.0067350.2665300.00538711-1.142090e-14
437180836.062.01.352277e-123.11247.093.120.07.00.0070310.2694600.00566111-3.592900e-14
15278734.0116.03.485911e-133.0100.068.710.04.00.0060170.2641890.004902-1-1-2.426630e-14
1458764-21.0373.01.689526e-122.951148.0130.733.018.00.0079330.2734600.00619912-1.010000e-14
1314106-5.039.02.188905e-132.8430.067.620.07.00.0060330.2643500.00495111-1.535070e-14
504809-75.0638.05.190861e-123.270123.0163.557.067.00.0088960.2796600.006921-1-1-6.380700e-14
2079144-12.0189.03.163148e-123.324128.0130.417.06.00.0077700.2760350.006158-1-1-8.864400e-14
2491359-2.029.04.034647e-133.2400.070.63.02.00.0060080.2646710.004932-1-13.285140e-14
SYM/H_INDEX_nT1-M_AE_nT400kmDensityAveDragCoefDAILY_SUNSPOT_NO_DAILY_F10.7_3-H_KP*10_3-H_AP_nTSOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
1776371-2.010.06.745130e-132.96310.074.30.00.00.0061110.2654120.005037-1-1-2.890940e-14
3962926-13.0395.05.308904e-132.94216.075.627.012.00.0062690.2646900.00512911-1.121400e-14
660167-4.053.05.432904e-133.0210.067.23.02.00.0059720.2640160.004905112.424610e-14
4353917-11.039.03.223227e-123.41592.0134.410.04.00.0084290.2770600.00641512-4.757300e-14
2406192-22.0150.02.553664e-122.95857.094.323.09.00.0068740.2699820.005601-1-1-2.090220e-13
4307519-18.0101.02.479557e-123.28392.0123.530.015.00.0078230.2729300.00615011-5.563400e-14
4671162.029.01.617551e-123.03637.097.43.02.00.0075660.2699600.006180-1-1-1.764300e-14
876786-4.017.06.634675e-132.90411.072.23.02.00.0060570.2651280.004973-1-13.611770e-14
1779373-1.06.07.546553e-132.9240.075.50.00.00.0061470.2656280.005054-1-1-5.069510e-14
6926740.024.04.882056e-133.2990.068.80.00.00.0060500.2645270.004951-1-11.750811e-13